Sunghwan Joo
Impact in
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- Building Energy and Comfort Optimization
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- Smart Grid Energy Management
- Energy Load and Power Forecasting
- Advanced DC-DC Converters
- Advanced Memory and Neural Computing
- IoT-based Smart Home Systems
Papers in
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- Radio Frequency Integrated Circuit Design 2
- Advanced Memory and Neural Computing 2
- Ferroelectric and Negative Capacitance Devices 1
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- Adversarial Robustness in Machine Learning 3
- Co-authors
- Taesup Moon (3 shared papers)Hyoseop Lee (1 shared paper)Wonjong Rhee (1 shared paper)Seong‐Ook Jung (7 shared papers)Sumin Lee (4 shared papers)Juyeon Heo (2 shared papers)Tae Woo Oh (2 shared papers)Adrian Weller (1 shared paper)
- Journals
- IEEE Journal of Solid-State Circuits (2 papers)IEEE Access (1 paper)IEEE Sensors Journal (1 paper)arXiv (Cornell University) (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)
- Partner nations
- South KoreaUnited Kingdom
In The Last Decade
Sunghwan Joo
12 papers receiving 174 citations
Peers
Comparison fields: 5 of 35
- Building and Construction 35
- Electrical and Electronic Engineering 133
- Control and Systems Engineering 34
- Artificial Intelligence 31
- Computer Vision and Pattern Recognition 18
Countries citing papers authored by Sunghwan Joo
This map shows the geographic impact of Sunghwan Joo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sunghwan Joo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sunghwan Joo more than expected).
Fields of papers citing papers by Sunghwan Joo
This network shows the impact of papers produced by Sunghwan Joo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sunghwan Joo. The network helps show where Sunghwan Joo may publish in the future.
Co-authors
The 16 scholars most cited alongside Sunghwan Joo, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 96 | |
| 2 | 2018 | 27 | |
| 3 | Fooling Neural Network Interpretations via Adversarial Model Manipulation | 2019 | 11 |
| 4 | 2020 | 10 | |
| 5 | 2019 | 6 | |
| 6 | 2018 | 6 | |
| 7 | 2022 | 6 | |
| 8 | 2022 | 5 | |
| 9 | 2023 | 3 | |
| 10 | 2021 | 3 | |
| 11 | 2021 | 2 | |
| 12 | 2016 | 1 |
About Sunghwan Joo
Sunghwan Joo is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Networks and Communications and Media Technology, having authored 12 papers that have together received 176 indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (3 papers), Analog and Mixed-Signal Circuit Design (2 papers), Radio Frequency Integrated Circuit Design (2 papers), Advanced Image Processing Techniques (2 papers), Image Processing Techniques and Applications (2 papers), Advanced Memory and Neural Computing (2 papers), Ferroelectric and Negative Capacitance Devices (1 paper) and COVID-19 diagnosis using AI (1 paper). The work is most often cited by research in Building and Construction (35 citations), Electrical and Electronic Engineering (133 citations), Control and Systems Engineering (34 citations), Artificial Intelligence (31 citations) and Computer Vision and Pattern Recognition (18 citations). Sunghwan Joo has collaborated with scholars based in South Korea and United Kingdom. Frequent co-authors include Taesup Moon, Hyoseop Lee, Wonjong Rhee, Seong‐Ook Jung, Sumin Lee, Juyeon Heo, Tae Woo Oh, Adrian Weller, Young Seok Jung and Kyomin Sohn. Their work appears in journals such as IEEE Journal of Solid-State Circuits, IEEE Access, IEEE Sensors Journal, arXiv (Cornell University) and Proceedings of the AAAI Conference on Artificial Intelligence.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.